learning_rate: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Precision Micro	Recall Micro	Recall At 3	Recall At 5
0.8293	1.0	44	0.7935	0.2010	0.0348	0.1365	0.3811	0.2169	0.2724
0.7495	2.0	88	0.7544	0.2326	0.0326	0.1605	0.4226	0.2708	0.3669
0.7045	3.0	132	0.7379	0.2970	0.0539	0.2481	0.3698	0.3581	0.4528
0.7120	4.0	176	0.7184	0.2972	0.0682	0.2139	0.4868	0.3732	0.4926
0.6766	5.0	220	0.7017	0.2996	0.0870	0.2097	0.5245	0.3405	0.4634
0.6569	6.0	264	0.6817	0.3559	0.1129	0.2664	0.5358	0.4208	0.5801
0.6366	7.0	308	0.6658	0.3408	0.1129	0.2380	0.6	0.4301	0.5406
0.6025	8.0	352	0.6517	0.3713	0.1286	0.2719	0.5849	0.4378	0.5888
0.5755	9.0	396	0.6468	0.3695	0.1210	0.2700	0.5849	0.4205	0.5651
0.5695	10.0	440	0.6354	0.3807	0.1382	0.2707	0.6415	0.4596	0.5838
0.5580	11.0	484	0.6348	0.3709	0.1433	0.2603	0.6453	0.4295	0.5954
0.5485	12.0	528	0.6277	0.3636	0.1307	0.2562	0.6264	0.4272	0.5432
0.5319	13.0	572	0.6196	0.3865	0.1482	0.2752	0.6491	0.4596	0.6022
0.5063	14.0	616	0.6214	0.3850	0.1577	0.2717	0.6604	0.4495	0.6057
0.4967	15.0	660	0.6181	0.3709	0.1342	0.2655	0.6151	0.4433	0.5817
0.4838	16.0	704	0.6162	0.3866	0.1522	0.2788	0.6302	0.4558	0.6095
0.4641	17.0	748	0.6123	0.3952	0.1641	0.2887	0.6264	0.4912	0.6328
0.4619	18.0	792	0.6073	0.3902	0.1466	0.2826	0.6302	0.4836	0.6314
0.4555	19.0	836	0.6082	0.3753	0.1515	0.2672	0.6302	0.4717	0.5845
0.4339	20.0	880	0.6087	0.3810	0.1541	0.2696	0.6491	0.4714	0.5820
0.4439	21.0	924	0.6103	0.3942	0.1372	0.2908	0.6113	0.4842	0.5956
0.4251	22.0	968	0.6090	0.4034	0.1550	0.2984	0.6226	0.4856	0.6207
0.4196	23.0	1012	0.6000	0.3693	0.1596	0.2587	0.6453	0.4644	0.6045
0.4222	24.0	1056	0.6066	0.3985	0.1540	0.2939	0.6189	0.4801	0.6192
0.4026	25.0	1100	0.6083	0.4039	0.1541	0.2980	0.6264	0.4912	0.6189
0.4028	26.0	1144	0.6082	0.3975	0.1538	0.2945	0.6113	0.4801	0.6342
0.4056	27.0	1188	0.6093	0.3937	0.1522	0.2903	0.6113	0.4829	0.6196
0.4020	28.0	1232	0.6052	0.4050	0.1544	0.3038	0.6075	0.5037	0.6213
0.3867	29.0	1276	0.6090	0.3965	0.1504	0.2961	0.6	0.4912	0.6145
0.3840	30.0	1320	0.6033	0.3932	0.1551	0.2890	0.6151	0.4912	0.6233
0.3730	31.0	1364	0.6056	0.3995	0.1522	0.2985	0.6038	0.5023	0.6050
0.3661	32.0	1408	0.6063	0.4131	0.1578	0.3100	0.6189	0.5190	0.6414
0.3630	33.0	1452	0.6058	0.4090	0.1573	0.3054	0.6189	0.5044	0.6150
0.3707	34.0	1496	0.6058	0.4044	0.1560	0.3004	0.6189	0.4981	0.6233
0.3607	35.0	1540	0.6031	0.4160	0.1629	0.3114	0.6264	0.5190	0.6525
0.3588	36.0	1584	0.6069	0.4046	0.1548	0.3042	0.6038	0.5051	0.6200
0.3591	37.0	1628	0.6069	0.4106	0.1553	0.3092	0.6113	0.5127	0.6117
0.3647	38.0	1672	0.6062	0.4050	0.1541	0.3038	0.6075	0.5023	0.6217
0.3483	39.0	1716	0.6058	0.4090	0.1565	0.3064	0.6151	0.4995	0.6133
0.3508	40.0	1760	0.6063	0.4111	0.1574	0.3087	0.6151	0.5044	0.6217

Framework versions

Transformers 5.13.0
Pytorch 2.12.1+cu130
Datasets 4.8.5
Tokenizers 0.22.2

CIRCL/vulnerability-attack-technique-classification-pilot

vulnerability-attack-technique-classification-pilot

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

CIRCL/
vulnerability-attack-technique-classification-pilot